A Physical Modeling Approach to Determine Protein Structures from Paramagnetic NMR Measurements

Abstract

Paramagnetic NMR approaches to protein structure determination can provide critical long-range information that may be difficult to obtain using standard NOE measurements, particularly for larger proteins. The paramagnetic relaxation enhancement (PRE) effect can be used to infer distances between residues and site-specifically engineered paramagnetic spin labels. These measurements are both sparse, due to the cost of making spin-labeled mutants, and ambiguous, due to the inherent flexibility of the spin-label. Here, we demonstrate the application of MELD (Modeling Employing Limited Data), a Bayesian approach combing sparse and ambiguous data with physical modeling, to the structure determination of a Calmodulin-peptide complex. Ten single-cysteine mutants were labeled with the common MTSL spin label, and PRE measurements were taken. Our approach can identify dominant conformations within 4 Å RMSD of the reference crystal structure using an implicit solvent model, while simulations without PRE data yield no near-native conformations. Inclusion of an explicit solvent significantly improves the model in comparison to the reference.